テスト

Testing output of the coding.

<<<<<<< HEAD

## Loading the packages and setting adjustment
suppressMessages(source('function/libs.R'))

=======

## Loading the packages and setting adjustment
suppressMessages(source(paste0(getwd(),'/function/libs.R')))

>>>>>>> 8c8a740b2768e7c450af85ace180489785115091

1. DT, 2. DT, 3. DT: #DT

## Read the datasets Refer to **Testing efficiency of coding.Rmd** at chunk
## `get-data-summary-table-2.1`
years <- seq(2011, 2015)

## Here I take the majority leagues setting profile which are 'league-10-12'
## fMYPriceB = Back with vigorish price; fMYPriceL = Lay with vigorish price
## Here we term as Fair Odds
lProfile <- c(AH = 0.1, OU = 0.12)

mbase <- readfirmDatasets(years = years) %>% arrfirmDatasets(., lProfile = lProfile)

## In order to analyse the AHOU, here I need to filter out all soccer matches
## other than AHOU. (For example : Corners, Total League Goals etc.)  the
## stakes amount display as $1 = $10,000
#'@ mbase$datasets[!(mbase$datasets$Home %in% mbase$corners)|!(mbase$datasets$Away %in% mbase$corners),]
dat <- mbase$datasets %>% filter((!Home %in% mbase$others) | (!Away %in% mbase$others)) %>% 
    mutate(Stakes = Stakes/10000, Return = Return/10000, PL = PL/10000, Month = month(ymd(Date), 
        label = TRUE))

#'@ pander(head(dat)) # exactly same layout with kable(x)
#'@ kable(head(dat)) ## example of the dataset in the research paper

<<<<<<< HEAD
dat %>% datatable(., caption = "Table 2.1.1 : Firm A Staking Data", extensions = c("ColReorder", 
    "ColVis", "TableTools"), options = list(dom = "TC<\"clear\">rlfrtip", colVis = list(exclude = c(0), 
    activate = "mouseover"), tableTools = list(sSwfPath = copySWF(pdf = TRUE)), 
    scrollX = TRUE, scrollCollapse = TRUE))

rm(years, readfirmDatasets, arrfirmDatasets)
rm(mbase)  ## We need to scrap the livescore data based on the raw data mbase without filter, but this is not the point in this research paper.

Please refer to Natural Language Analysis to see the firm A staking sample dataset.

summary(lm(Return ~ pHKRange, data = dat))

======= #'@ dat %>% datatable(.,caption='Table 2.1.1 : Firm A Staking Data',extensions=c('ColReorder','ColVis','TableTools'),options=list(dom='TC<'clear'>rlfrtip',colVis=list(exclude=c(0),activate='mouseover'),tableTools=list(sSwfPath=copySWF(pdf=TRUE)),scrollX=TRUE,scrollCollapse=TRUE)) rm(years, readfirmDatasets, arrfirmDatasets) rm(mbase) ## We need to scrap the livescore data based on the raw data mbase without filter, but this is not the point in this research paper.